On the parallel complexity of discrete relaxation in constraint satisfaction networks
Artificial Intelligence
GRASP—a new search algorithm for satisfiability
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
On forward checking for non-binary constraint satisfaction
Artificial Intelligence
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
BerkMin: A Fast and Robust Sat-Solver
Proceedings of the conference on Design, automation and test in Europe
A comparison of ATMS and CSP techniques
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
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We investigate in this work a generalization of the known CNF representation which allows an efficient Boolean encoding for n-ary CSPs. We show that the space complexity of the Boolean encoding is identical to the one of the classical CSP representation and introduce a new inference rule whose application until saturation achieves arc-consistency in a linear time complexity for n-ary CSPs expressed in the Boolean encoding. Two enumerative methods for the Boolean encoding are studied: the first one (equivalent to MAC in CSPs) maintains full arc-consistency on each node of the search tree while the second (equivalent to FC in CSPs) performs partial arc-consistency on each node. Both methods are experimented and compared on some instances of the Ramsey problem and randomly generated 3-ary CSPs and promising results are obtained.